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Monitoring path nearest neighbor in road networks
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International Conference on Management of Data archive
Proceedings of the 35th SIGMOD international conference on Management of data table of contents
Providence, Rhode Island, USA
SESSION: Research session 15: nearest neighbor search table of contents
Pages 591-602  
Year of Publication: 2009
ISBN:978-1-60558-551-2
Authors
Zaiben Chen  The University of Queensland, Brisbane, Australia
Heng Tao Shen  The University of Queensland, Brisbane, Australia
Xiaofang Zhou  The University of Queensland, Brisbane, Australia
Jeffrey Xu Yu  The Chinese University of Hong Kong, Hong Kong, China
Sponsors
ACM: Association for Computing Machinery
SIGMOD: ACM Special Interest Group on Management of Data
Publisher
ACM  New York, NY, USA
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ABSTRACT

This paper addresses the problem of monitoring the k nearest neighbors to a dynamically changing path in road networks. Given a destination where a user is going to, this new query returns the k-NN with respect to the shortest path connecting the destination and the user's current location, and thus provides a list of nearest candidates for reference by considering the whole coming journey. We name this query the k-Path Nearest Neighbor query (k-PNN). As the user is moving and may not always follow the shortest path, the query path keeps changing. The challenge of monitoring the k-PNN for an arbitrarily moving user is to dynamically determine the update locations and then refresh the k-PNN efficiently. We propose a three-phase Best-first Network Expansion (BNE) algorithm for monitoring the k-PNN and the corresponding shortest path. In the searching phase, the BNE finds the shortest path to the destination, during which a candidate set that guarantees to include the k-PNN is generated at the same time. Then in the verification phase, a heuristic algorithm runs for examining candidates' exact distances to the query path, and it achieves significant reduction in the number of visited nodes. The monitoring phase deals with computing update locations as well as refreshing the k-PNN in different user movements. Since determining the network distance is a costly process, an expansion tree and the candidate set are carefully maintained by the BNE algorithm, which can provide efficient update on the shortest path and the k-PNN results. Finally, we conduct extensive experiments on real road networks and show that our methods achieve satisfactory performance.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Zaiben Chen: colleagues
Heng Tao Shen: colleagues
Xiaofang Zhou: colleagues
Jeffrey Xu Yu: colleagues